Deep learning of bismuth telluride crystal size and grain boundaries for predictive performance

A computer implemented method, comprising:

• training a machine learning module with a plurality of training datasets, each training dataset comprising a scanning electron microscope (SEM) image of a semiconductor wafer, an energy-dispersive spectroscopy (EDS/EDAX) image of the wafer, an electron backscatter diffraction (EBSD) image of the wafer, and zT values of semiconductor components of the wafer;
• and
• processing an input dataset of another semiconductor wafer through the machine learning module, the input dataset of the other wafer comprising an SEM image of the other semiconductor wafer, an EDS/EDAX image of the other wafer, and an EBSD image of the other wafer;
• and
• predicting zT values of semiconductor components of the other wafer based on the processing of the input dataset of the other wafer through the machine learning module.

A non-transitory computer readable medium comprising instructions that, when executed by a processor, direct the processor to:

• train a machine learning module with a plurality of training datasets, each training dataset comprising a scanning electron microscope (SEM) image of a semiconductor wafer, an energy-dispersive spectroscopy (EDS/EDAX) image of the wafer, an electron backscatter diffraction (EBSD) image of the wafer, and zT values of semiconductor components of the wafer;
• and
• process an input dataset of another semiconductor wafer through the machine learning module, the input dataset of the other wafer comprising an SEM image of the other semiconductor wafer, an EDS/EDAX image of the other wafer, and an EBSD image of the other wafer;
• and
• predict zT values of semiconductor components of the other wafer based on the processing of the input dataset of the other wafer through the machine learning module.